Jin Wanying, Rezaeipanah Amin
Tianjin Transportation Technical College, Tianjin, 300380, China.
Department of Computer Engineering, Persian Gulf University, Bushehr, Iran.
Sci Rep. 2025 May 27;15(1):18513. doi: 10.1038/s41598-025-03621-4.
Fog computing extends cloud services to the edge of the network, enabling low-latency processing and improved resource utilization, which are crucial for real-time Internet of Things (IoT) applications. However, efficient task allocation remains a significant challenge due to the dynamic and heterogeneous nature of fog environments. Traditional task scheduling methods often fail to manage uncertainty in task requirements and resource availability, leading to suboptimal performance. In this paper, we propose a novel approach, DTA-FLE (Dynamic Task Allocation in Fog computing using a Fuzzy Logic Enhanced approach), which leverages fuzzy logic to handle the inherent uncertainty in task scheduling. Our method dynamically adapts to changing network conditions, optimizing task allocation to improve efficiency, reduce latency, and enhance overall system performance. Unlike conventional approaches, DTA-FLE introduces a novel hierarchical scheduling mechanism that dynamically adapts to real-time network conditions using fuzzy logic, ensuring optimal task allocation and improved system responsiveness. Through simulations using the iFogSim framework, we demonstrate that DTA-FLE outperforms conventional techniques in terms of execution time, resource utilization, and responsiveness, making it particularly suitable for real-time IoT applications within hierarchical fog-cloud architectures.
雾计算将云服务扩展到网络边缘，实现低延迟处理并提高资源利用率，这对于实时物联网（IoT）应用至关重要。然而，由于雾环境的动态性和异构性，高效的任务分配仍然是一项重大挑战。传统的任务调度方法往往无法应对任务需求和资源可用性的不确定性，导致性能欠佳。在本文中，我们提出了一种新颖的方法，即DTA-FLE（使用模糊逻辑增强方法的雾计算动态任务分配），该方法利用模糊逻辑来处理任务调度中固有的不确定性。我们的方法能够动态适应不断变化的网络条件，优化任务分配以提高效率、降低延迟并增强整体系统性能。与传统方法不同，DTA-FLE引入了一种新颖的分层调度机制，该机制使用模糊逻辑动态适应实时网络条件，确保最佳任务分配并提高系统响应能力。通过使用iFogSim框架进行模拟，我们证明DTA-FLE在执行时间、资源利用率和响应能力方面优于传统技术，使其特别适用于分层雾云架构中的实时物联网应用。
